diff --git a/examples/ted_en_zh/t0/conf/transformer_joint_noam.yaml b/examples/ted_en_zh/t0/conf/transformer_joint_noam.yaml
index f8dc383f0..9c1ac91a9 100644
--- a/examples/ted_en_zh/t0/conf/transformer_joint_noam.yaml
+++ b/examples/ted_en_zh/t0/conf/transformer_joint_noam.yaml
@@ -99,6 +99,7 @@ decoding:
   alpha: 2.5
   beta: 0.3
   beam_size: 10
+  word_reward: 0.7
   cutoff_prob: 1.0
   cutoff_top_n: 0
   num_proc_bsearch: 8
diff --git a/paddlespeech/s2t/exps/u2_st/model.py b/paddlespeech/s2t/exps/u2_st/model.py
index 91390afe5..20bb31f53 100644
--- a/paddlespeech/s2t/exps/u2_st/model.py
+++ b/paddlespeech/s2t/exps/u2_st/model.py
@@ -441,10 +441,7 @@ class U2STTester(U2STTrainer):
             "".join(chr(t) for t in text[:text_len])
             for text, text_len in zip(texts, texts_len)
         ]
-        # from IPython import embed
-        # import os
-        # embed()
-        # os._exit(0)
+        
         hyps = self.model.decode(
             audio,
             audio_len,
@@ -458,6 +455,7 @@ class U2STTester(U2STTrainer):
             cutoff_top_n=cfg.cutoff_top_n,
             num_processes=cfg.num_proc_bsearch,
             ctc_weight=cfg.ctc_weight,
+            word_reward=cfg.word_reward,
             decoding_chunk_size=cfg.decoding_chunk_size,
             num_decoding_left_chunks=cfg.num_decoding_left_chunks,
             simulate_streaming=cfg.simulate_streaming)
diff --git a/paddlespeech/s2t/models/u2_st/u2_st.py b/paddlespeech/s2t/models/u2_st/u2_st.py
index 76c50150e..a83e67078 100644
--- a/paddlespeech/s2t/models/u2_st/u2_st.py
+++ b/paddlespeech/s2t/models/u2_st/u2_st.py
@@ -315,6 +315,7 @@ class U2STBaseModel(nn.Layer):
             speech: paddle.Tensor,
             speech_lengths: paddle.Tensor,
             beam_size: int=10,
+            word_reward: float=0.0,
             decoding_chunk_size: int=-1,
             num_decoding_left_chunks: int=-1,
             simulate_streaming: bool=False, ) -> paddle.Tensor:
@@ -378,6 +379,7 @@ class U2STBaseModel(nn.Layer):
 
             # 2.2 First beam prune: select topk best prob at current time
             top_k_logp, top_k_index = logp.topk(beam_size)  # (B*N, N)
+            top_k_logp += word_reward
             top_k_logp = mask_finished_scores(top_k_logp, end_flag)
             top_k_index = mask_finished_preds(top_k_index, end_flag, self.eos)
 
@@ -528,6 +530,7 @@ class U2STBaseModel(nn.Layer):
                cutoff_top_n: int,
                num_processes: int,
                ctc_weight: float=0.0,
+               word_reward: float=0.0,
                decoding_chunk_size: int=-1,
                num_decoding_left_chunks: int=-1,
                simulate_streaming: bool=False):
@@ -569,6 +572,7 @@ class U2STBaseModel(nn.Layer):
                 feats,
                 feats_lengths,
                 beam_size=beam_size,
+                word_reward=word_reward,
                 decoding_chunk_size=decoding_chunk_size,
                 num_decoding_left_chunks=num_decoding_left_chunks,
                 simulate_streaming=simulate_streaming)